Analyse der Matoma-HaNS-Daten

1 Setup

1.1 R-Pakete starten

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library(targets)
library(tidyverse)
library(ggokabeito)
library(easystats)
library(gt)
library(ggfittext)
library(scales)

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theme_set(theme_minimal())

1.2 Roh-Daten laden und inspizieren

JSON-Daten wurden nicht importiert, da offenbar nur redundante Daten enthalten sind.

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tar_load(data_all_fct)

1.2.1 Dimension

Der Roh-Datensatz verfügt über

3623 Zeilen
7873 Spalten (Dubletten und Spalten mit Bildern bereits entfernt)

Jede Zeile entspricht einem “Visit”.

1.3 Datensatz nur User

Entfernt man Developer, Admins und Lecturers aus dem Roh-Datensatz so bleiben weniger Zeilen übrig:

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tar_load(data_users_only)

3389 Zeilen
7873 Spalten

1.4 Datensatz mit Anzahl der Aktionen pro User

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tar_load(count_action)

1.5 Zeitraum (Beginn, Ende) der Daten

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tar_load(paths)

Laut config.yaml ist das aktuelle Semester 24-ss.

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tar_load(time_minmax)

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time_minmax |> 
  summarise(time_min = min(time_min),
            time_max = max(time_max)) |> 
  gt()

time_min	time_max
2024-03-04 09:40:13	2024-06-06 11:06:14

Diese Statistik wurde auf Basis des Datenobjekts data_slim berechnet.

1.6 Statistiken

1.6.1 Mit den 499er-Daten

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count_action |> 
  describe_distribution(n_max) |> 
  gt() |> 
  fmt_number(columns = where(is.numeric),
             decimals = 2)

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
n_max	657.18	1,247.39	679.00	10.00	11,816.00	3.43	15.79	3,187.00	0.00

1.6.2 Ohne die 499er-Daten

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count_action2 <- 
count_action |> 
  filter(n_max != 499) 

count_action2 |> 
  describe_distribution(n_max) |> 
  gt() |> 
  fmt_number(columns = where(is.numeric),
             decimals = 2)

Variable	Mean	SD	IQR	Min	Max	Skewness	Kurtosis	n	n_Missing
n_max	657.23	1,247.58	681.25	10.00	11,816.00	3.43	15.78	3,186.00	0.00

1.7 Verteilung

1.7.1 Mit den 499er-Daten

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count_action_avg = mean(count_action$n_max)
count_action_sd = sd(count_action$n_max)

count_action |> 
  ggplot() +
  geom_histogram(aes(x = n_max)) +
  labs(x = "Anzahl von Aktionen pro Visit",
       y = "n",
       caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
  theme_minimal() +
  geom_vline(xintercept = count_action_avg,
             color = palette_okabe_ito()[1]) +
  geom_segment(x = count_action_avg-count_action_sd,
               y = 0,
               xend = count_action_avg + count_action_sd,
               yend = 0,
               color = palette_okabe_ito()[2],
               size = 2) +
  annotate("label", x = count_action_avg, y = 1500, label = "MW") +
  annotate("label", x = count_action_avg + count_action_sd, y = 0, label = "SD")

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  #geom_label(aes(x = count_action_avg), y = 1, label = "Mean")

Mittelwert der Aktionen pro Visit: 657.18.
SD der Aktionen pro Visit: 1247.39.

1.7.2 Ohne 499er-Daten

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count_action_avg2 = mean(count_action2$n_max)
count_action_sd2 = sd(count_action2$n_max)

count_action2 |> 
  ggplot() +
  geom_histogram(aes(x = n_max)) +
  labs(x = "Anzahl von Aktionen pro Visit",
       y = "n",
       title = "Verteilung der User-Aktionen pro Visit",
       caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") +
  theme_minimal() +
  geom_vline(xintercept = count_action_avg2,
             color = palette_okabe_ito()[1]) +
  geom_segment(x = count_action_avg-count_action_sd2,
               y = 0,
               xend = count_action_avg2 + count_action_sd2,
               yend = 0,
               color = palette_okabe_ito()[2],
               size = 2) +
  annotate("label", x = count_action_avg2, y = 1500, label = "MW", vjust = "top") +
  annotate("label", x = count_action_avg2 + count_action_sd2, y = 0, label = "SD", vjust = "bottom")

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  #geom_label(aes(x = count_action_avg), y = 1, label = "Mean")

Mittelwert der Aktionen pro Visit: 657.23.
SD der Aktionen pro Visit: 1247.58.

2 Zeit pro Visit

Die Visit-Zeit wurde auf 600 Min. begrenzt.

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tar_load(time_spent)

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time_spent <- 
  time_spent |> 
  mutate(t_min = as.numeric(time_diff, units = "mins")) |> 
  filter(t_min < 600)

2.1 Verweildauer-Statistiken in Sekunden

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time_spent |> 
  summarise(
    mean_time_diff = round(mean(time_diff), 2),
    sd_time_diff = sd(time_diff),
    min_time_diff = min(time_diff),
    max_time_diff = max(time_diff)
  ) |> 
  gt() |> 
  fmt_number(columns = everything(),
             decimals = 2)

mean_time_diff	sd_time_diff	min_time_diff	max_time_diff
1329.38	2,212.42	0	19946

2.2 Verweildauer-Statistiken in Minuten

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time_spent |> 
  summarise(
    mean_t_min = mean(t_min),
    sd_t_min = sd(t_min),
    min_t_min = min(t_min),
    max_t_min = max(t_min)
  ) |> 
  gt() |> 
  fmt_number(columns = everything(),
             decimals = 2)

mean_t_min	sd_t_min	min_t_min	max_t_min
22.16	36.87	0.00	332.43

2.3 Visualisierung der Verweildauer

2.3.1 bins=20

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time_spent |> 
  ggplot(aes(x = t_min)) +
  geom_histogram() +
  scale_x_time() +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in HaNS pro Visit in Minuten")

2.3.2 bins=100

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time_spent |> 
  ggplot(aes(x = t_min)) +
  geom_histogram(binwidth = 5) +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in Minuten",
       title = "Verweildauer in HaNS pro Visit",
       caption = "binwidth = 5 Min.")

2.3.3 Zeitdauer begrenzt auf 1-120 Min.

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time_spent2 <- 
time_spent |> 
  filter(t_min > 1, t_min < 120) 

time_spent2 |> 
  ggplot(aes(x = t_min)) +
  geom_histogram(binwidth = 10) +
  theme_minimal() +
  labs(y = "n",
       x = "Verweildauer in HaNS pro Visit in Minuten",
       title = "Verweildauer begrenzt auf 1-120 Minuten",
       caption = "bindwidth = 10 Min.")

3 Was machen die User?

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tar_load(count_action_type)

3.1 Statistiken

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count_action_type |> 
  count(category, sort = TRUE) |> 
  gt()

category	n
NA	1032265
video	138257
click_slideChange	11116
visit_page	10486
login	866
click_topic	829
Search Results Count	813
in_media_search	620
Kanäle	512
Medien	415
click_channelcard	293
GESOA	260

3.2 Verteilung

3.2.1 Rohwerte

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count_action_type |> 
  count(category, sort = TRUE) |> 
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "User-Aktion",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie"
  ) +
  theme_minimal() +
  scale_x_continuous(labels = scales::comma)

3.2.2 Log-Skalierung

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count_action_type |> 
  count(category, sort = TRUE) |> 
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "Anazhl der User-Aktionen",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie",
    caption = "Log10-Skala"
  ) +
  theme_minimal() +
  scale_x_log10()

4 An welchen Tagen und zu welcher Zeit kommen die User zu HaNS?

4.1 Setup

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tar_load(time_visit_wday)

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# Define a vector with the names of the days of the week
# Note: Adjust the start of the week (Sunday or Monday) as per your requirement
days_of_week <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")

# Replace numbers with day names
time_visit_wday$dow2 <- factor(days_of_week[time_visit_wday$dow],
                               levels = days_of_week)

4.2 HaNS-Login nach Uhrzeit

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time_visit_wday |> 
  as_tibble() |> 
  count(hour) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(
    title = "HaNS-Nutzer sind keine Frühaufsteher",
    x = "Uhrzeit",
    y = "Anteil"
  )

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 # coord_polar()

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time_visit_wday |> 
  as_tibble() |> 
  count(hour) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  theme_minimal() +
  coord_polar()

4.3 Verteilung der HaNS-Besuche nach Wochentagen

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time_visit_wday |> 
  as_tibble() |> 
  count(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = dow2, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
       x = "Wochentag",
       y = "Anteil")

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 # coord_polar()

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time_visit_wday |> 
  as_tibble() |> 
  count(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = dow2, y = prop)) +
  geom_col() +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen",
       x = "Wochentag",
       y = "Anteil")  +
  coord_polar()

4.3.1 HaNS-Login nach Wochentagen Uhrzeit

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time_visit_wday |> 
  as_tibble() |> 
  count(dow2, hour) |> 
  group_by(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  facet_wrap(~ dow2) +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
       x = "Wochentag",
       y = "Anteil")

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 # coord_polar()

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time_visit_wday |> 
  as_tibble() |> 
  count(dow2, hour) |> 
  group_by(dow2) |> 
  mutate(prop = n/sum(n)) |> 
  ggplot(aes(x = hour, y = prop)) +
  geom_col() +
  facet_wrap(~ dow2) +
  theme_minimal() +
  labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten",
       x = "Wochentag",
       y = "Anteil") +
  coord_polar()

4.4 Anzahl der Visits nach Datum (Tagen) und Uhrzeit (bin2d)

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time2 <- 
time_visit_wday |> 
  ungroup() |> 
  mutate(date = as.Date(date_time))

time2 |> 
  ggplot(aes(x = date, y = hour)) +
  geom_bin2d(binwidth = c(1, 1)) + # (1 day, 1 hour)
  scale_x_date(date_breaks = "1 month") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c() +
  labs(caption = "Each x-bin maps to one week")

4.5 Anzahl der Visits nach Datum (Wochen) und Uhrzeit (bin2d)

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time2 |> 
  ggplot(aes(x = date, y = hour)) +
  geom_bin2d(binwidth = c(7, 1)) +  # 1 week, 1 hour
  scale_x_date(date_breaks = "1 week", date_labels = "%W") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c()  +
  labs(x = "Week number in 2023/2024",
       caption = "Each x-bin maps to one week")

4.6 Anzahl der Visits nach Datum (Wochen) und Wochentag (bin2d)

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time2 |> 
  ggplot(aes(x = date, y = dow)) +
  geom_bin2d(binwidth = c(7, 1)) +  # 1 week, 1 hour
  scale_x_date(date_breaks = "1 week", date_labels = "%W") +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c()  +
  labs(x = "Week number in 2023/2024",
       caption = "Each x-bin maps to one week",
       y = "Day of Week") +
  scale_y_continuous(breaks = 1:7)

5 KI-Gebrauch

5.1 Welcher Anteil der Nutzenden klickt auf ein Wort im Transkript?

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tar_load(data_slim)

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data_slim |> 
  filter(type == "subtitle") |> 
  filter(!is.na(value) & value != "") |> 
  count(click_transcript_word = str_detect(value, "click_transcript_word")) |> 
  mutate(prop = n/sum(n)) |> 
  gt()

click_transcript_word	n	prop
FALSE	190359	0.998929488
TRUE	204	0.001070512

5.2 … Aufteilung nach Monaten

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tar_load(ai_transcript_clicks_per_month)

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ai_transcript_clicks_per_month |> 
  gt()

click_transcript_word	n
2024-3
FALSE	48326
2024-4
FALSE	102929
2024-5
FALSE	31776
2024-6
FALSE	7532
NA-NA
FALSE	190359
TRUE	204

--- title: "Analyse der Matoma-HaNS-Daten" toc: true number-sections: true format: html: theme: lumen embed-resources: true toc: true toc-location: right toc-depth: 3 number-sections: true code-fold: true code-summary: "Show the code" code-tools: true execute: warning: false cache: true --- # Setup ## R-Pakete starten ```{r load-libs} library(targets) library(tidyverse) library(ggokabeito) library(easystats) library(gt) library(ggfittext) library(scales) ``` ```{r} theme_set(theme_minimal()) ``` ## Roh-Daten laden und inspizieren JSON-Daten wurden nicht importiert, da offenbar nur redundante Daten enthalten sind. ```{r load-data-all-f} tar_load(data_all_fct) ``` ### Dimension Der Roh-Datensatz verfügt über - `r nrow(data_all_fct)` Zeilen - `r ncol(data_all_fct)` Spalten (Dubletten und Spalten mit Bildern bereits entfernt) Jede Zeile entspricht einem "Visit". ## Datensatz nur User Entfernt man Developer, Admins und Lecturers aus dem Roh-Datensatz so bleiben weniger Zeilen übrig: ```{r load-data-users-only} tar_load(data_users_only) ``` - `r nrow(data_users_only)` Zeilen - `r ncol(data_users_only)` Spalten ## Datensatz mit Anzahl der Aktionen pro User ```{r load-count-action} tar_load(count_action) ``` ## Zeitraum (Beginn, Ende) der Daten ```{r} tar_load(paths) ``` Laut `config.yaml` ist das aktuelle Semester `r paths$this_semester`. ```{r} tar_load(time_minmax) ``` ```{r} time_minmax |> summarise(time_min = min(time_min), time_max = max(time_max)) |> gt() ``` Diese Statistik wurde auf Basis des Datenobjekts `data_slim` berechnet. ## Statistiken ### Mit den 499er-Daten ```{r} count_action |> describe_distribution(n_max) |> gt() |> fmt_number(columns = where(is.numeric), decimals = 2) ``` ### Ohne die 499er-Daten ```{r} count_action2 <- count_action |> filter(n_max != 499) count_action2 |> describe_distribution(n_max) |> gt() |> fmt_number(columns = where(is.numeric), decimals = 2) ``` ## Verteilung ### Mit den 499er-Daten ```{r plot-count-action} count_action_avg = mean(count_action$n_max) count_action_sd = sd(count_action$n_max) count_action |> ggplot() + geom_histogram(aes(x = n_max)) + labs(x = "Anzahl von Aktionen pro Visit", y = "n", caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") + theme_minimal() + geom_vline(xintercept = count_action_avg, color = palette_okabe_ito()[1]) + geom_segment(x = count_action_avg-count_action_sd, y = 0, xend = count_action_avg + count_action_sd, yend = 0, color = palette_okabe_ito()[2], size = 2) + annotate("label", x = count_action_avg, y = 1500, label = "MW") + annotate("label", x = count_action_avg + count_action_sd, y = 0, label = "SD") #geom_label(aes(x = count_action_avg), y = 1, label = "Mean") ``` - Mittelwert der Aktionen pro Visit: `r round(count_action_avg, 2)`. - SD der Aktionen pro Visit: `r round(count_action_sd, 2)`. ### Ohne 499er-Daten ```{r plot-count-action-2} count_action_avg2 = mean(count_action2$n_max) count_action_sd2 = sd(count_action2$n_max) count_action2 |> ggplot() + geom_histogram(aes(x = n_max)) + labs(x = "Anzahl von Aktionen pro Visit", y = "n", title = "Verteilung der User-Aktionen pro Visit", caption = "Der vertikale Strich zeigt den Mittelwert; der horizontale die SD") + theme_minimal() + geom_vline(xintercept = count_action_avg2, color = palette_okabe_ito()[1]) + geom_segment(x = count_action_avg-count_action_sd2, y = 0, xend = count_action_avg2 + count_action_sd2, yend = 0, color = palette_okabe_ito()[2], size = 2) + annotate("label", x = count_action_avg2, y = 1500, label = "MW", vjust = "top") + annotate("label", x = count_action_avg2 + count_action_sd2, y = 0, label = "SD", vjust = "bottom") #geom_label(aes(x = count_action_avg), y = 1, label = "Mean") ``` - Mittelwert der Aktionen pro Visit: `r round(count_action_avg2, 2)`. - SD der Aktionen pro Visit: `r round(count_action_sd2, 2)`. # Zeit pro Visit Die Visit-Zeit wurde auf 600 Min. begrenzt. ```{r load-time-spent} tar_load(time_spent) ``` ```{r} time_spent <- time_spent |> mutate(t_min = as.numeric(time_diff, units = "mins")) |> filter(t_min < 600) ``` ## Verweildauer-Statistiken in Sekunden ```{r comp-diff-time-stats} time_spent |> summarise( mean_time_diff = round(mean(time_diff), 2), sd_time_diff = sd(time_diff), min_time_diff = min(time_diff), max_time_diff = max(time_diff) ) |> gt() |> fmt_number(columns = everything(), decimals = 2) ``` ## Verweildauer-Statistiken in Minuten ```{r} time_spent |> summarise( mean_t_min = mean(t_min), sd_t_min = sd(t_min), min_t_min = min(t_min), max_t_min = max(t_min) ) |> gt() |> fmt_number(columns = everything(), decimals = 2) ``` ## Visualisierung der Verweildauer ### bins=20 ```{r plot-time-spent1} time_spent |> ggplot(aes(x = t_min)) + geom_histogram() + scale_x_time() + theme_minimal() + labs(y = "n", x = "Verweildauer in HaNS pro Visit in Minuten") ``` ### bins=100 ```{r plot-time-spent2} time_spent |> ggplot(aes(x = t_min)) + geom_histogram(binwidth = 5) + theme_minimal() + labs(y = "n", x = "Verweildauer in Minuten", title = "Verweildauer in HaNS pro Visit", caption = "binwidth = 5 Min.") ``` ### Zeitdauer begrenzt auf 1-120 Min. ```{r plot-time-spent3} time_spent2 <- time_spent |> filter(t_min > 1, t_min < 120) time_spent2 |> ggplot(aes(x = t_min)) + geom_histogram(binwidth = 10) + theme_minimal() + labs(y = "n", x = "Verweildauer in HaNS pro Visit in Minuten", title = "Verweildauer begrenzt auf 1-120 Minuten", caption = "bindwidth = 10 Min.") ``` # Was machen die User? ```{r tar-load-count-action-type} tar_load(count_action_type) ``` ## Statistiken ```{r} count_action_type |> count(category, sort = TRUE) |> gt() ``` ## Verteilung ### Rohwerte ```{r vis-count-action-type} count_action_type |> count(category, sort = TRUE) |> ggplot(aes(y = reorder(category, n), x = n)) + geom_col() + geom_bar_text() + labs( x = "User-Aktion", y = "Aktion", title = "Anzahl der User-Aktionen nach Kategorie" ) + theme_minimal() + scale_x_continuous(labels = scales::comma) ``` ### Log-Skalierung ```{r vis-count-action-type-log} #| fig-width: 9 count_action_type |> count(category, sort = TRUE) |> ggplot(aes(y = reorder(category, n), x = n)) + geom_col() + geom_bar_text() + labs( x = "Anazhl der User-Aktionen", y = "Aktion", title = "Anzahl der User-Aktionen nach Kategorie", caption = "Log10-Skala" ) + theme_minimal() + scale_x_log10() ``` # An welchen Tagen und zu welcher Zeit kommen die User zu HaNS? ## Setup ```{r} tar_load(time_visit_wday) ``` ```{r} # Define a vector with the names of the days of the week # Note: Adjust the start of the week (Sunday or Monday) as per your requirement days_of_week <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday") # Replace numbers with day names time_visit_wday$dow2 <- factor(days_of_week[time_visit_wday$dow], levels = days_of_week) ``` ## HaNS-Login nach Uhrzeit ```{r vis-hans-login-hour} time_visit_wday |> as_tibble() |> count(hour) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = hour, y = prop)) + geom_col() + theme_minimal() + labs( title = "HaNS-Nutzer sind keine Frühaufsteher", x = "Uhrzeit", y = "Anteil" ) # coord_polar() ``` ```{r vis-hans-login-hour-polar} time_visit_wday |> as_tibble() |> count(hour) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = hour, y = prop)) + geom_col() + theme_minimal() + coord_polar() ``` ## Verteilung der HaNS-Besuche nach Wochentagen ```{r vis-hans-login-wday-bar} time_visit_wday |> as_tibble() |> count(dow2) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = dow2, y = prop)) + geom_col() + theme_minimal() + labs(title = "Verteilung der HaNS-Logins nach Wochentagen", x = "Wochentag", y = "Anteil") # coord_polar() ``` ```{r vis-hans-login-wday-polar} time_visit_wday |> as_tibble() |> count(dow2) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = dow2, y = prop)) + geom_col() + theme_minimal() + labs(title = "Verteilung der HaNS-Logins nach Wochentagen", x = "Wochentag", y = "Anteil") + coord_polar() ``` ### HaNS-Login nach Wochentagen Uhrzeit ```{r vis-hans-login-wday-hour} time_visit_wday |> as_tibble() |> count(dow2, hour) |> group_by(dow2) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = hour, y = prop)) + geom_col() + facet_wrap(~ dow2) + theme_minimal() + labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten", x = "Wochentag", y = "Anteil") # coord_polar() ``` ```{r vis-hans-login-wday-hour-polar} #| fig-width: 9 #| fig-asp: 1.5 time_visit_wday |> as_tibble() |> count(dow2, hour) |> group_by(dow2) |> mutate(prop = n/sum(n)) |> ggplot(aes(x = hour, y = prop)) + geom_col() + facet_wrap(~ dow2) + theme_minimal() + labs(title = "Verteilung der HaNS-Logins nach Wochentagen und Uhrzeiten", x = "Wochentag", y = "Anteil") + coord_polar() ``` ## Anzahl der Visits nach Datum (Tagen) und Uhrzeit (bin2d) ```{r} time2 <- time_visit_wday |> ungroup() |> mutate(date = as.Date(date_time)) time2 |> ggplot(aes(x = date, y = hour)) + geom_bin2d(binwidth = c(1, 1)) + # (1 day, 1 hour) scale_x_date(date_breaks = "1 month") + theme(legend.position = "bottom") + scale_fill_viridis_c() + labs(caption = "Each x-bin maps to one week") ``` ## Anzahl der Visits nach Datum (Wochen) und Uhrzeit (bin2d) ```{r} time2 |> ggplot(aes(x = date, y = hour)) + geom_bin2d(binwidth = c(7, 1)) + # 1 week, 1 hour scale_x_date(date_breaks = "1 week", date_labels = "%W") + theme(legend.position = "bottom") + scale_fill_viridis_c() + labs(x = "Week number in 2023/2024", caption = "Each x-bin maps to one week") ``` ## Anzahl der Visits nach Datum (Wochen) und Wochentag (bin2d) ```{r} time2 |> ggplot(aes(x = date, y = dow)) + geom_bin2d(binwidth = c(7, 1)) + # 1 week, 1 hour scale_x_date(date_breaks = "1 week", date_labels = "%W") + theme(legend.position = "bottom") + scale_fill_viridis_c() + labs(x = "Week number in 2023/2024", caption = "Each x-bin maps to one week", y = "Day of Week") + scale_y_continuous(breaks = 1:7) ``` # KI-Gebrauch ## Welcher Anteil der Nutzenden klickt auf ein Wort im Transkript? ```{r} tar_load(data_slim) ``` ```{r} data_slim |> filter(type == "subtitle") |> filter(!is.na(value) & value != "") |> count(click_transcript_word = str_detect(value, "click_transcript_word")) |> mutate(prop = n/sum(n)) |> gt() ``` ## ... Aufteilung nach Monaten ```{r} tar_load(ai_transcript_clicks_per_month) ``` ```{r} ai_transcript_clicks_per_month |> gt() ```